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XNNPACK

XNNPACK is a highly optimized solution for neural network inference on ARM, x86, WebAssembly, and RISC-V platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch, ONNX Runtime, and MediaPipe.

Supported Architectures

  • ARM64 on Android, iOS, macOS, Linux, and Windows
  • ARMv7 (with NEON) on Android
  • ARMv6 (with VFPv2) on Linux
  • x86 and x86-64 (up to AVX512) on Windows, Linux, macOS, Android, and iOS simulator
  • WebAssembly MVP
  • WebAssembly SIMD
  • WebAssembly Relaxed SIMD (experimental)
  • RISC-V (RV32GC and RV64GC)

Operator Coverage

XNNPACK implements the following neural network operators:

  • 2D Convolution (including grouped and depthwise)
  • 2D Deconvolution (AKA Transposed Convolution)
  • 2D Average Pooling
  • 2D Max Pooling
  • 2D ArgMax Pooling (Max Pooling + indices)
  • 2D Unpooling
  • 2D Bilinear Resize
  • 2D Depth-to-Space (AKA Pixel Shuffle)
  • Add (including broadcasting, two inputs only)
  • Subtract (including broadcasting)
  • Divide (including broadcasting)
  • Maximum (including broadcasting)
  • Minimum (including broadcasting)
  • Multiply (including broadcasting)
  • Squared Difference (including broadcasting)
  • Global Average Pooling
  • Channel Shuffle
  • Fully Connected
  • Abs (absolute value)
  • Bankers' Rounding (rounding to nearest, ties to even)
  • Ceiling (rounding to integer above)
  • Clamp (includes ReLU and ReLU6)
  • Convert (includes fixed-point and half-precision quantization and dequantization)
  • Copy
  • ELU
  • Floor (rounding to integer below)
  • HardSwish
  • Leaky ReLU
  • Negate
  • Sigmoid
  • Softmax
  • Square
  • Transpose
  • Truncation (rounding to integer towards zero)
  • PReLU

All operators in XNNPACK support NHWC layout, but additionally allow custom stride along the Channel dimension. Thus, operators can consume a subset of channels in the input tensor, and produce a subset of channels in the output tensor, providing a zero-cost Channel Split and Channel Concatenation operations.

Performance

Mobile phones

The table below presents single-threaded performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.

Model Pixel, ms Pixel 2, ms Pixel 3a, ms
FP32 MobileNet v1 1.0X 82 86 88
FP32 MobileNet v2 1.0X 49 53 55
FP32 MobileNet v3 Large 39 42 44
FP32 MobileNet v3 Small 12 14 14

The following table presents multi-threaded (using as many threads as there are big cores) performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.

Model Pixel, ms Pixel 2, ms Pixel 3a, ms
FP32 MobileNet v1 1.0X 43 27 46
FP32 MobileNet v2 1.0X 26 18 28
FP32 MobileNet v3 Large 22 16 24
FP32 MobileNet v3 Small 7 6 8

Benchmarked on March 27, 2020 with end2end_bench --benchmark_min_time=5 on an Android/ARM64 build with Android NDK r21 (bazel build -c opt --config android_arm64 :end2end_bench) and neural network models with randomized weights and inputs.

Raspberry Pi

The table below presents multi-threaded performance of XNNPACK library on three generations of MobileNet models and three generations of Raspberry Pi boards.

Model RPi Zero W (BCM2835), ms RPi 2 (BCM2836), ms RPi 3+ (BCM2837B0), ms RPi 4 (BCM2711), ms RPi 4 (BCM2711, ARM64), ms
FP32 MobileNet v1 1.0X 3919 302 114 72 77
FP32 MobileNet v2 1.0X 1987 191 79 41 46
FP32 MobileNet v3 Large 1658 161 67 38 40
FP32 MobileNet v3 Small 474 50 22 13 15
INT8 MobileNet v1 1.0X 2589 128 46 29 24
INT8 MobileNet v2 1.0X 1495 82 30 20 17

Benchmarked on Feb 8, 2022 with end2end-bench --benchmark_min_time=5 on a Raspbian Buster build with CMake (./scripts/build-local.sh) and neural network models with randomized weights and inputs. INT8 inference was evaluated on per-channel quantization schema.

Minimum build requirements

  • C11
  • C++14
  • Python 3

Publications

Ecosystem

Machine Learning Frameworks

Acknowledgements

XNNPACK is a based on QNNPACK library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.


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